ENERGY EFFICIENCY IN NEURON NETWORKS: PROBLEMS OF OPTIMIZING LARGE MODELS

This article analyzes technical and economic problems associated with increased energy consumption by large neural networks. The fact that modern AI models have trillions of parameters requires enormous computing power in their training and inference processes, which leads to increased energy consumption and increased infrastructure costs. The article examines the technical essence of quantization, practical results, and its role in optimizing large models.